README

Données

Variables explicatives pour le modèle de distribution

rf = readRDS("rfmod__all.rds")
rf.ericar = rf$rfmod
library(randomForest)

## randomForest 4.7-1.1

## Type rfNews() to see new features/changes/bug fixes.

## 
## Attaching package: 'randomForest'

## The following object is masked from 'package:ggplot2':
## 
##     margin

## The following object is masked from 'package:dplyr':
## 
##     combine

varImpPlot(rf.ericar)

Prediction / projection

library(leaflet)
library(leafem)
library(stars)
library(sf)
library(raster)

sites_all = read.csv("_data_prod/sites_all.csv")

# rf.predict.ericar.p= read_stars("_data_prod/rf.predict.ericar_prob_wm30_wm.tif", proxy = FALSE,  package="stars")
rf.values.p = raster("_data_prod/rf.predict.ericar_prob_wm30_wm.tif")
rasproj2 = raster::aggregate(rf.values.p, fac = 3)
rasproj2[rasproj2<0.01] = NA
rasproj2[rasproj2>1] = 1
rf.predict.ericar.p3 = st_as_stars(rasproj2)

leaflet(sites_all)%>%
  addProviderTiles("OpenStreetMap.HOT")%>%
  leafem::addGeoRaster(rf.predict.ericar.p3, opacity = 0.8, colorOptions = colorOptions(palette = colorRampPalette(c("lightblue", "violet", "darkviolet"), space="Lab"))) %>%
  setView(lng=5.5,lat=45,zoom=6) %>%
  addCircleMarkers(lng = ~lon_wgs84, lat = ~lat_wgs84, popup = ~date, radius = 0.3, opacity = 0.8, color = "black")